Workflow · Production

Amazon Dialogue Boost uses on-device AI audio separation to enhance movie and TV dialogue clarity

The problem

Hard-to-hear dialogue in movies and TV has worsened over the last decade as complex multi-channel theater sound systems do not translate well to home playback configurations, leaving viewers — especially the nearly 20% of the global population with hearing loss — unable to understand dialogue without also amplifying background music and sound effects.

Workflow diagram · grounded in source
1
Time-frequency transformation
ai_action
“the incoming audio stream is transformed into a time-frequency representation, which maps energy in different frequency bands against time”
2
Neural network speech separation
ai_action
“a neural network trained on thousands of hours of speaking conditions including various languages, accents, recording circumstances, combinations of sound effects, and background noises. This model analyzes the time-frequency representat…”
3
Intelligent dialogue mixing
ai_action
“The final stage is intelligent mixing, which goes beyond simple volume adjustment. The system combines multiple techniques to enhance dialogue while preserving the artistic intent of the original mix: it identifies speech-dominant audio …”
4
Enhanced audio delivered
output
“Dialogue Boost enhances the clarity of movie and TV dialogue while adaptively suppressing background music and sound effects”
Reported outcome

The on-device Dialogue Boost model runs within device constraints while maintaining nearly identical performance to cloud-based techniques, with over 86% of participants preferring the enhanced audio and 100% feature approval among users with hearing loss.

Reported metrics
Participant preference for enhanced audioover 86%
Feature approval among users with hearing loss100%
Model size reduction via knowledge distillationless than 1% of their size
Operations compared to larger modelsless than 1% as many operations
Show all 6 reported metrics
participant preference for enhanced audioover 86%
feature approval among users with hearing loss100%
model size reduction via knowledge distillationless than 1% of their size
operations compared to larger modelsless than 1% as many operations
model parameters compared to larger modelsabout 2% as many model parameters
listening effort for users with hearing losssignificantly reduced listening effort
Reported stack
Dialogue BoostEchoFire TVPrime Video
Source
https://www.amazon.science/blog/dialogue-boost-how-amazon-is-using-ai-to-enhance-tv-and-movie-dialogue
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

The on-device Dialogue Boost model runs within device constraints while maintaining nearly identical performance to cloud-based techniques, with over 86% of participants preferring the enhanced audio and 100% feature…

What tools did this team use?

Dialogue Boost, Echo, Fire TV, Prime Video.

What results were reported?

Participant preference for enhanced audio: over 86%; Feature approval among users with hearing loss: 100%; Model size reduction via knowledge distillation: less than 1% of their size; Operations compared to larger models: less than 1% as many operations (source-reported, not independently verified).

How is this workflow AI workflow structured?

Time-frequency transformation → Neural network speech separation → Intelligent dialogue mixing → Enhanced audio delivered.